@inproceedings{sirbu-etal-2025-multimatch,
    title = "{M}ulti{M}atch: Multihead Consistency Regularization Matching for Semi-Supervised Text Classification",
    author = "Sirbu, Iustin  and
      Popovici, Robert-Adrian  and
      Caragea, Cornelia  and
      Trausan-Matu, Stefan  and
      Rebedea, Traian",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.139/",
    pages = "2792--2808",
    ISBN = "979-8-89176-332-6",
    abstract = "We introduce **MultiMatch**, a novel semi-supervised learning (SSL) algorithm combining the paradigms of co-training and consistency regularization with pseudo-labeling. At its core, MultiMatch features a three-fold pseudo-label weighting module designed for selecting and filtering pseudo-labels based on head agreement and model confidence, and weighting them according to the perceived classification difficulty. This novel module enhances and unifies three existing techniques $-$ heads agreement from **Multi**head Co-training, self-adaptive thresholds from Free**Match**, and Average Pseudo-Margins from Margin**Match** $-$ resulting in a holistic approach that improves robustness and performance in SSL settings.Experimental results on benchmark datasets highlight the superior performance of MultiMatch, i.e., MultiMatch achieves state-of-the-art results on 8 out of 10 setups from 5 natural language processing datasets and ranks first according to the Friedman test among 21 methods. Furthermore, MultiMatch demonstrates exceptional robustness in highly imbalanced settings, outperforming the second-best approach by 3.26{\%}, a critical advantage for real-world text classification tasks. Our code is available on GitHub."
}